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3 pages, 176 KiB

Open AccessEditorial

Applying Modern Meta-Heuristics in Intelligent Systems

by Peng-Yeng Yin

Appl. Sci. 2022, 12(19), 9746; https://doi.org/10.3390/app12199746 - 28 Sep 2022

Viewed by 1073

Abstract

Engineering and business problems are increasingly impenetrable due to the new economics triggered by big data, artificial intelligence, and the Internet of things [...] Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

Research

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21 pages, 3402 KiB

Open AccessArticle

A Multi-Strategy Improved Sparrow Search Algorithm for Solving the Node Localization Problem in Heterogeneous Wireless Sensor Networks

by Hang Zhang, Jing Yang, Tao Qin, Yuancheng Fan, Zetao Li and Wei Wei

Appl. Sci. 2022, 12(10), 5080; https://doi.org/10.3390/app12105080 - 18 May 2022

Cited by 16 | Viewed by 1951

Abstract

Aiming at the problems of slow convergence and low accuracy of the traditional sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA) was proposed. Firstly, the golden sine algorithm was introduced in the location update of producers to improve the global [...] Read more.

Aiming at the problems of slow convergence and low accuracy of the traditional sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA) was proposed. Firstly, the golden sine algorithm was introduced in the location update of producers to improve the global optimization capability of SSA. Secondly, the idea of individual optimality in the particle swarm algorithm was introduced into the position update of investigators to improve the convergence speed. At the same time, a Gaussian disturbance was introduced to the global optimal position to prevent the algorithm from falling into the local optimum. Then, the performance of the ISSA was evaluated on 23 benchmark functions, and the results indicate that the improved algorithm has better global optimization ability and faster convergence. Finally, ISSA was used for the node localization of HWSNs, and the experimental results show that the localization algorithm with ISSA has a smaller average localization error than that of the localization algorithm with other meta-heuristic algorithms. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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22 pages, 729 KiB

Open AccessArticle

A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem

by Xavier Sánchez-Díaz, José Carlos Ortiz-Bayliss, Ivan Amaya, Jorge M. Cruz-Duarte, Santiago Enrique Conant-Pablos and Hugo Terashima-Marín

Appl. Sci. 2021, 11(21), 10209; https://doi.org/10.3390/app112110209 - 31 Oct 2021

Cited by 7 | Viewed by 3229

Abstract

Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform [...] Read more.

Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform single heuristics when evaluated in isolation. When hyper-heuristics are selected among existing algorithms, they map problem states into suitable solvers. Unfortunately, identifying the features that accurately describe the problem state—and thus allow for a proper mapping—requires plenty of domain-specific knowledge, which is not always available. This work proposes a simple yet effective hyper-heuristic model that does not rely on problem features to produce such a mapping. The model defines a fixed sequence of heuristics that improves the solving process of knapsack problems. This research comprises an analysis of feature-independent hyper-heuristic performance under different learning conditions and different problem sets. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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22 pages, 7872 KiB

Open AccessArticle

Thread-Aware Mechanism to Enhance Inter-Node Load Balancing for Multithreaded Applications on NUMA Systems

by Mei-Ling Chiang and Wei-Lun Su

Appl. Sci. 2021, 11(14), 6486; https://doi.org/10.3390/app11146486 - 14 Jul 2021

Cited by 4 | Viewed by 2310

Abstract

NUMA multi-core systems divide system resources into several nodes. When an imbalance in the load between cores occurs, the kernel scheduler’s load balancing mechanism then migrates threads between cores or across NUMA nodes. Remote memory access is required for a thread to access [...] Read more.

NUMA multi-core systems divide system resources into several nodes. When an imbalance in the load between cores occurs, the kernel scheduler’s load balancing mechanism then migrates threads between cores or across NUMA nodes. Remote memory access is required for a thread to access memory on the previous node, which degrades performance. Threads to be migrated must be selected effectively and efficiently since the related operations run in the critical path of the kernel scheduler. This study focuses on improving inter-node load balancing for multithreaded applications. We propose a thread-aware selection policy that considers the distribution of threads on nodes for each thread group while migrating one thread for inter-node load balancing. The thread is selected for which its thread group has the least exclusive thread distribution, and thread members are distributed more evenly on nodes. This has less influence on data mapping and thread mapping for the thread group. We further devise several enhancements to eliminate superfluous evaluations for multithreaded processes, so the selection procedure is more efficient. The experimental results for the commonly used PARSEC 3.0 benchmark suite show that the modified Linux kernel with the proposed selection policy increases performance by 10.7% compared with the unmodified Linux kernel. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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20 pages, 3397 KiB

Open AccessArticle

Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

by Erik Cuevas, Héctor Becerra, Héctor Escobar, Alberto Luque-Chang, Marco Pérez, Heba F. Eid and Mario Jiménez

Appl. Sci. 2021, 11(8), 3430; https://doi.org/10.3390/app11083430 - 12 Apr 2021

Cited by 5 | Viewed by 1940

Abstract

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present [...] Read more.

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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17 pages, 4238 KiB

Open AccessArticle

A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

by Hector Rico-Garcia, Jose-Luis Sanchez-Romero, Antonio Jimeno-Morenilla and Hector Migallon-Gomis

Appl. Sci. 2021, 11(2), 818; https://doi.org/10.3390/app11020818 - 16 Jan 2021

Cited by 5 | Viewed by 2260

Abstract

The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) [...] Read more.

The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?”. At present, with the development of Internet of Things (IoT) devices and increased capabilities of sensors, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the aim is to provide a solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm Teacher Learner Based Optimization (TLBO). In addition, to improve performance, the solution is implemented by means of a parallel graphics processing unit (GPU) architecture, specifically a Compute Unified Device Architecture (CUDA) implementation. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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25 pages, 2293 KiB

Open AccessArticle

Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

by Peng-Yeng Yin, Po-Yen Chen, Ying-Chieh Wei and Rong-Fuh Day

Appl. Sci. 2020, 10(24), 8961; https://doi.org/10.3390/app10248961 - 15 Dec 2020

Cited by 5 | Viewed by 2178

Abstract

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are [...] Read more.

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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16 pages, 1252 KiB

Open AccessFeature PaperArticle

Identification of Opinion Leaders and Followers—A Case Study of Green Energy and Low Carbons

by Chun-Che Huang, Wen-Yau Liang, Po-An Chen and Yi-Chin Chan

Appl. Sci. 2020, 10(23), 8416; https://doi.org/10.3390/app10238416 - 26 Nov 2020

Cited by 6 | Viewed by 3336

Abstract

In recent years, with the development of Web2.0, enterprises, government agencies, and traditional news media, which have been positively influenced by opinion leaders, have been dedicated to understanding leaders’ opinions on the web in order to seek convergence. Specifically, with the increase of [...] Read more.

In recent years, with the development of Web2.0, enterprises, government agencies, and traditional news media, which have been positively influenced by opinion leaders, have been dedicated to understanding leaders’ opinions on the web in order to seek convergence. Specifically, with the increase of environmental awareness, the introduction of green energy and carbon reduction technology has become an important issue. Consequently, studies identifying opinion leaders and followers who are interested in green energy and low carbon have become important. This study aims to find a solution that can identify the characteristics of opinion leaders and followers that can be widely used, which will help certain public policies or issues to be more effectively disseminated in the future. To model the characteristics of opinion leaders and their influence on followers, this study uses a dual matrix. The interaction patterns are recognized among opinion leaders and followers, with the aim of developing public policy to promote green energy and low carbon emissions. A case is studied to validate the superiority of the proposed solution approach. With the proposed approach, a (business) organization can identify and access opinion leaders and their followers. Through communication, these organizations can absorb strain and preserve functions despite the presence of adversity. This study also clearly demonstrates its contribution and novelty through comparisons with the existing alternative method. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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24 pages, 552 KiB

Open AccessArticle

A Memetic Algorithm for the Cumulative Capacitated Vehicle Routing Problem Including Priority Indexes

by Samuel Nucamendi-Guillén, Diego Flores-Díaz, Elias Olivares-Benitez and Abraham Mendoza

Appl. Sci. 2020, 10(11), 3943; https://doi.org/10.3390/app10113943 - 5 Jun 2020

Cited by 13 | Viewed by 2986

Abstract

This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments [...] Read more.

This paper studies the Cumulative Capacitated Vehicle Routing Problem, including Priority Indexes, a variant of the classical Capacitated Vehicle Routing Problem, which serves the customers according to a certain level of preference. This problem can be effectively implemented in commercial and public environments where customer service is essential, for instance, in the delivery of humanitarian aid or in waste collection systems. For this problem, we aim to minimize two objectives simultaneously, the total latency and the total tardiness of the system. A Mixed Integer formulation is developed and solved using the AUGMECON2 approach to obtain true efficient Pareto fronts. However, as expected, the use of commercial software was able to solve only small instances, up to 15 customers. Therefore, two versions of a Memetic Algorithm with Random Keys (MA-RK) were developed to solve the problem. The computational results show that both algorithms provided good solutions, although the second version obtained denser and higher quality Pareto fronts. Later, both algorithms were used to solve larger instances (20–100 customers). The results were mixed in terms of quality but, in general, the MA-RK v2 consistently outperforms the first version. The models and algorithms proposed in this research provide useful insights for the decision-making process and can be applied to solve a wide variety of business situations where economic, customer service, environmental, and social concerns are involved. Full article

(This article belongs to the Special Issue Applied (Meta)-Heuristic in Intelligent Systems)

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